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arXiv 2302.02860math.NAcs.LGcs.NAphysics.comp-ph
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局部收敛输入神经网络求解二维Maxwell方程

Solving Maxwell's Equation in 2D with Neural Networks with Local Converging Inputs

Harris Cobb, Hwi Lee, Yingjie Liu

摘要:

在本文中,我们应用神经网络与本地收敛输入(NNLCI),最初在[arXiv:2109.09316]中介绍,解决二维麦克斯韦方程周围的完美电导体(PEC)。网络的输入由在两个粗网格上计算的方程的低成本数值解的局部补丁组成,并且输出是在局部补丁的中心处的更精确的解。我们应用最近开发的二阶有限差分法[arXiv:2209.00740]来生成输入和训练数据,该数据捕获在给定终端时间处PEC的电磁波散射。NNLCI的优点是,一旦训练,它提供了一个有效的替代昂贵的高分辨率传统的数值方法,我们的数值实验表明,计算复杂性节省了$8^3$的时空网格点的数量方面的一个因素。与现有的研究工作应用神经网络直接求解偏微分方程相比,我们的方法利用了麦克斯韦方程在输入解补丁的局部依赖域,因此更简单,但仍然强大。我们证明,我们可以训练我们的神经网络上的一些PEC预测准确的解决方案,不同的PEC与不同的几何形状从任何训练的例子。

英文摘要:

In this paper we apply neural networks with local converging inputs (NNLCI), originally introduced in [arXiv:2109.09316], to solve the two dimensional Maxwell's equation around perfect electric conductors (PECs). The input to the networks consist of local patches of low cost numerical solutions to the equation computed on two coarse grids, and the output is a more accurate solution at the center of the local patch. We apply the recently developed second order finite difference method [arXiv:2209.00740] to generate the input and training data which captures the scattering of electromagnetic waves off of a PEC at a given terminal time. The advantage of NNLCI is that once trained it offers an efficient alternative to costly high-resolution conventional numerical methods; our numerical experiments indicate the computational complexity saving by a factor of $8^3$ in terms of the number of spatial-temporal grid points. In contrast with existing research work on applying neural networks to directly solve PDEs, our method takes advantage of the local domain of dependence of the Maxwell's equation in the input solution patches, and is therefore simpler, yet still robust. We demonstrate that we can train our neural network on some PECs to predict accurate solutions to different PECs with quite different geometries from any of the training examples.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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